Apparatus, method, computer-readable storage medium for non-destructive inspection of bicycle based on analyzing amount of scale value change

ABSTRACT

A non-destructive inspection apparatus is provided. The non-destructive inspection apparatus includes at least one memory configured to store commands for performing predetermined operations, and at least one processor operatively coupled to the at least one memory and configured to execute the commands. The at least one processor is configured to obtain information on a transmission amount of an X-ray by emitting the X-ray to a part of a bicycle, generate a gray scale image based on the information on the transmission, measure an amount of change in a gray value from one end to the other end of the part of the bicycle represented in the gray scale image along an extending direction of the part, and detect an area in which the amount of change in the gray value is equal to or greater than a threshold, as an abnormal area.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.17/551,627, filed on Dec. 15, 2021, which claims priority to KoreanPatent Application No. 10-2021-0123908, filed on Sep. 16, 2021. Theentire contents of these application on which the priority is based areincorporated herein by reference.

TECHNICAL FIELD

The present disclosure relates to a technique for performingnon-destructive inspection of a part of a bicycle based on a scalechange analysis of a transmission amount of X-ray.

BACKGROUND

A bicycle is a means of transportation that a person steps on a pedal tomove, and as the bicycle is made of a light and strong material, theefficiency and stability of movement increase. Accordingly, recently, abicycle made of a carbon composite material, which is light in weightand relatively strong, has been developed and manufactured.

Meanwhile, among parts of a bicycle, there are parts made so thin that athickness of the carbon composite material is less than 1 mm, and thus,pores may occur inside the carbon composite material during themanufacturing process. Accordingly, the weight or impact of a user maycause damage to the part during use, and cracks easily occur in thepart.

Abnormal portions such as the pores or cracks have a great influence onstability of the bicycle. For example, a defect in the abnormal portionmay lead to a serious accident due to damage while the bicycle isrunning, and the bicycle having the abnormal portion may be traded as anormal bicycle in the second-hand market. Accordingly, a buyer who buysthe bicycle without being aware of these problems may have accidents ordamage.

However, the abnormal portion of the carbon composite material is veryfine, so it is difficult to identify the abnormal without an expert.Further, it is not possible to check the inside thereof withoutdisassembling and inspecting the joint portion of the bicycle, so thatthe inspection process also has a problem in that required time and costare considerable.

-   (Related Art) Korean Patent No. 10-2157233

SUMMARY

In view of the above, embodiments of the present disclosure provide atechnique for automatically detecting an abnormal portion of a bicyclethrough non-destructive inspection.

In particular, in order to automatically and accurately detect theabnormal portion, a technique for acquiring information on an analysistarget from the bicycle for easy analysis and a technique for analyzingthe meaning of the information by processing the acquired informationare required. To this end, the embodiments of the present disclosureprovide a pre-processing information acquisition process of generatingan image suitable for non-destructive inspection of carbon, and atechnology for detecting an abnormal portion of a part by processing theinformation obtained through the pre-processing information acquisitionprocess.

Technical objects to be achieved by the present disclosure are notlimited to those described above, and other technical objects notmentioned above may also be clearly understood from the descriptionsgiven below by those skilled in the art to which the present disclosurebelongs.

In accordance with an aspect of the present disclosure, there isprovided a non-destructive inspection apparatus including: at least onememory configured to store commands for performing predeterminedoperations; and at least one processor operatively coupled to the atleast one memory and configured to execute the commands. The at leastone processor is configured to: obtain information on a transmissionamount of an X-ray by emitting the X-ray to a part of a bicycle,generate a gray scale image based on the information on thetransmission, measure an amount of change in a gray value from one endto the other end of the part of the bicycle represented in the grayscale image along an extending direction of the part, and detect an areain which the amount of change in the gray value is equal to or greaterthan a threshold, as an abnormal area.

Further, the X-ray may be emitted with a voltage in a range from 60 kVto 70 kV, a current in a range from 11.0 mA to 12.0 mA, and a focalpoint (FOC) in a range from 0.4 mm to 1.0 mm so as to obtain the grayscale of the part of the bicycle formed of carbon.

Further, in generating the gray scale image, the processor may befurther configured to rescale an intensity of the transmission amount ofthe X-ray into an arbitrary unit intensity (a.u. intensity) between aminimum value of 0 and a maximum value of 3500, and select one templateof a first template, a second template, a third template, a fourthtemplate, and a fifth template depending on the part of the bicycle.When the first template is selected, only portions of the part havingrescaled intensities of the transmission amount of the X-ray rangingfrom 2216 to 2500 may be converted into the gray scale. When the secondtemplate is selected, only portions of the part having rescaledintensities of the transmission amount of the X-ray ranging from 2108 to3294 may be converted into the gray scale. When the third template isselected, only portions of the part having rescaled intensities of thetransmission amount of the X-ray ranging from 1893 to 2878 may beconverted into the gray scale. When the fourth template is selected,only portions of the part having rescaled intensities of thetransmission amount of the X-ray ranging from 1257 to 2878 may beconverted into the gray scale. Further, when the fifth template isselected, only portions of the part having rescaled intensities of thetransmission amount of the X-ray ranging from 31 to 2410 may beconverted into the gray scale.

Further, in measuring the amount of change in the gray value, theprocessor may be further configured to generate two-dimensional graphinformation in which the amount of change in the gray value is measuredwith an x-axis representing a movement distance of one point from theone end to the other end along the extending direction of the part and ay-axis representing the gray value of the gray scale measured when theone point is controlled to move on the x-axis.

Further, in detecting the abnormal area, when an amount of change in they-axis within a predetermined range of the x-axis is equal to or greaterthan a preset threshold, the processor may be further configured todetect, as the abnormal area, a portion corresponding to an x-axislength at which the amount of change in the y-axis that is equal to orgreater than the preset threshold is generated.

Further, in measuring the amount of change in the gray value, theprocessor may be further configured to generate a three-dimensionalgraph information in which the amount of change in the gray value ismeasured with a Z-axis representing a length of a cutting line cuttingthe part of the bicycle at the one end, an x-axis representing amovement distance of the cutting line from the one end to the other endalong the extending direction of the part, and a y-axis representing thegray value of the gray scale measured while the cutting line iscontrolled to move on the x-axis.

Further, in detecting the abnormal area, when an amount of change in they-axis within a predetermined range of the x-axis is equal to or greaterthan a preset threshold, the processor may be further configured todetect, as the abnormal area, a portion corresponding to an x-axislength at which the amount of change in the y-axis that is equal to orgreater than the preset threshold is generated.

Further, in detecting the abnormal area, the processor may be furtherconfigured to detect, as the abnormal area, a three-dimensional areaincluding the x-axis length and a z-axis length at which the amount ofchange in the y-axis is that is equal to or greater than the presetthreshold is generated.

Further, in obtaining the information on the transmission amount, theprocessor may be further configured to specify the part of the bicycleby inputting an entire image of the bicycle to a first neural networkmodel that is trained with an image data set for each part of thebicycle to which specification information for the corresponding part ofthe bicycle is mapped, and obtain information on the transmission amountof the X-ray for the specified part by adjusting a X-ray emissionposition to a position of the specified part. The specificationinformation may include information specifying a type of the part andinformation on a voltage, a current, a focal length of the X-ray presetto perform non-destructive inspection on the part.

Further, the first neural network may be trained based on an imagerecognition algorithm, and the image data set for each part of thebicycle may include a data set in which a frame, a wheel, and adrivetrain are labeled in portions of the image.

Further, the processor may be further configured to determine a type ofthe abnormal area based on an image of the abnormal area afterperforming the detecting of the abnormal area.

Further, in determining the type of the abnormal area, the processor maybe further configured to determine a class of the abnormal area byinputting the image of the abnormal area to a second neural networkmodel that is trained with an abnormal image data set for each part ofthe bicycle, and calculate a price of the bicycle by reflectingdepreciation information obtained based on the class of the abnormalarea to specification information of the part including the abnormalarea. The specification information may include information specifyingthe price of the part.

Further, the second neural network may be trained based on the imagerecognition algorithm, and the abnormal image data set for each part ofthe bicycle may include a data set in which breakage, repair,reinforcement, joint, and pores of the part are labeled in portions ofthe image.

In accordance with another aspect of the present disclosure, there isprovided a non-destructive inspection method performed by anon-destructive inspection apparatus, the non-destructive inspectionmethod including: acquiring information on a transmission amount of anX-ray by emitting the X-ray to a part of a bicycle; generating a grayscale image based on the information on the transmission amount;measuring an amount of change in a gray value from one end to the otherend of a part of the bicycle represented in the gray scale image alongan extending direction of the part; and detecting an area in which theamount of change in the gray value is equal to or greater than athreshold, as an abnormal area.

In accordance with still another aspect of the present disclosure, thereis provided a non-transitory computer-readable storage medium includingcomputer-executable instructions which cause, when executed by aprocessor, the processor to perform a non-destructive inspection methodperformed by a non-destructive inspection apparatus, the non-destructiveinspection method including: acquiring information on a transmissionamount of an X-ray by emitting the X-ray to a part of a bicycle;generating a gray scale image based on the information on thetransmission amount; measuring an amount of change in a gray value fromone end to the other end of a part of the bicycle represented in thegray scale image along an extending direction of the part; and detectingan area in which the amount of change in the gray value is equal to orgreater than a threshold, as an abnormal area.

According to the aspects of the present disclosure, it is possible tosimply detect an abnormal portion of the part without disassembling thebicycle, and it is possible to determine the type of the abnormalportion. Accordingly, it is possible to improve the inspection accuracywhile effectively shortening the inspection process during manufacturingof the bicycle.

In addition, by allowing a certified institution to use the technique ofthe present disclosure, it becomes possible to certify the quality ofbicycles in the second-hand market, thereby establishing a healthytrading culture and ensuring the safety of bicycle users. The technicaleffects of the present disclosure are not limited to the technicaleffects described above, and other technical effects not mentionedherein may be understood to those skilled in the art to which thepresent disclosure belongs from the description below.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a functional block diagram of a non-destructive inspectionapparatus according to one embodiment.

FIG. 2 is an exemplary diagram of a first neural network for classifyingparts of a bicycle included in an overall image of a bicycle accordingto one embodiment.

FIG. 3 is an exemplary diagram illustrating an image which is a resultof detecting an abnormal area of a part according to one exemplaryembodiment and is used for training a second neural network fordetermining a type of the abnormal area.

FIG. 4 is a flowchart of an operation in which the non-destructiveinspection apparatus performs a non-destructive inspection methodaccording to one embodiment.

FIG. 5A is an exemplary diagram of a gray scale image generated frominformation obtained by changing properties of X-rays for the sameobject.

FIG. 5B is an exemplary diagram of a gray scale image generated frominformation obtained by changing properties of X-rays for the sameobject.

FIG. 5C is an exemplary diagram of a gray scale image generated frominformation obtained by changing properties of X-rays for the sameobject.

FIG. 6 is an exemplary diagram of the gray scale images in which a partof the bicycle is converted into the gray scale by changing an intensityof the transmission amount of X-rays.

FIG. 7 is an exemplary diagram for explaining an operation of measuringthe amount of change in the gray value according to one embodiment whilecontrolling a cutting line located at one end of a part of a bicycle tomove to the other end of the part of the bicycle along an extendingdirection of the part.

FIG. 8A is an exemplary diagram for explaining an operation of detectinga part in a normal state according to one embodiment.

FIG. 8B is an exemplary diagram for explaining an operation of detectinga part in a normal state according to one embodiment.

FIG. 8C is an exemplary diagram for explaining an operation of detectinga part in a normal state according to one embodiment.

FIG. 9A is an exemplary diagram for explaining an operation of detectinga part including an abnormal area according to one embodiment.

FIG. 9B is an exemplary diagram for explaining an operation of detectinga part including an abnormal area according to one embodiment.

FIG. 9C is an exemplary diagram for explaining an operation of detectinga part including an abnormal area according to one embodiment.

FIG. 10 is an exemplary diagram of a three-dimensional graph informationgenerated based on the amount of change in the gray value measuredaccording to one embodiment.

FIG. 11 is an exemplary diagram for explaining an operation of measuringthe amount of change in the gray value while controlling a point locatedat one end of the part of the bicycle to move to the other end of thepart of the bicycle along the extending direction of the part, accordingto one embodiment.

DETAILED DESCRIPTION

Hereinafter, various embodiments of the present disclosure will bedescribed with reference to the accompanying drawings. However, itshould be understood that the specific embodiments are not intended tolimit the gist of the present disclosure to the specific embodiments;rather, it should be understood that the specific embodiments includeall of the modifications, equivalents, and/or alternatives of theembodiments of the present disclosure. Regarding the description of thedrawings, the same or similar constituting elements are given the sameor similar reference symbol numbers.

FIG. 1 is a functional block diagram of a non-destructive inspectionapparatus 100 according to one embodiment.

Referring to FIG. 1 , the non-destructive inspection apparatus 100according to one embodiment may include a memory 110, a processor 120,an input/output interface 130, and a communication interface 140.

The memory 110 may include an image database (DB) 111, a specificationinformation DB 112, a neural network DB 113, and a command DB 114.

The image DB 111 may store data for an entire image of a bicycle, animage of a part of the bicycle, an X-ray photographed image of thebicycle, and an image having a specified abnormal area of the X-rayphotographed images of the bicycle. The image stored in the image DB 111may be used for the training of a first neural network and a secondneural network, which will be described below.

The specification information DB 112 may store detailed information ofthe part of the bicycle. For example, detailed information of the partincludes a material, a use, a manufacturer, a production year, a brandof each part, a category, a year, an operation method, a color, asubstance, a model name, a size, a minimum recommended key, a maximumrecommended key, a geometry, an amount of carbon emitted at the time ofmanufacturing, a price, a voltage, a current, and a focal length of anX-ray that should be set to perform non-destructive inspection on thepart, and template information that should be set to convert from X-raytransmission amount information obtained for the part to a gray scale.

The neural network DB 113 may store the trained neural network. Forexample, the neural network DB 113 may include a first neural networkmodel that is trained with an image data set for each part of thebicycle to which specification information for the corresponding part ofthe bicycle is mapped. When a predetermined bicycle image is input tothe first neural network model, a class (for example, frame, wheel,drivetrain, or the like) of the part included in the bicycle image isspecified and the specification information of each part is output fromthe first neural network. For example, the neural network DB 113 mayinclude a second neural network model that is trained with the abnormalimage data set for each part of the bicycle, and when a predeterminedabnormal area image is input to the second neural network model, a class(for example, breakage, repair, reinforcement, joint, pores, or thelike) of the abnormal area is specified by the second neural networkmodel.

The command DB 114 may store commands for performing an operation of theprocessor 120. For example, the command DB 114 may store computer codefor performing operations corresponding to operations of the processor120 to be described below.

The processor 120 may control the overall operation of thenon-destructive inspection apparatus 100. The processor 120 may includean inspection module 121, a neural network module 122, an image module123, and a control module 124. The processor 120 may execute thecommands stored in the memory 110 to drive the inspection module 121,the neural network module 122, the image module 123, and the controlmodule 124.

The inspection module 121 may be interlocked with a non-destructiveimaging device (that is, X-RAY CT SCANNER) that performs imaging of aproduct using the X-ray, and may control the non-destructive imagingdevice. For example, the inspection module 121 may control thenon-destructive imaging device to emit the X-ray to a part of thebicycle, and obtain information on an amount of the X-rays transmittedthrough the parts of the bicycle. The non-destructive inspectionapparatus 100 may be connected to the non-destructive imaging device bywire or wireless.

The neural network module 122 may train and control the first neuralnetwork and the second neural network according to one embodiment.

FIG. 2 is an exemplary diagram of a first neural network for classifyingparts of the bicycle included in the overall image of the bicycleaccording to one embodiment.

Referring to FIG. 2 , the neural network module 122 may train the firstneural network by using an image recognition algorithm with trainingimages in each of which the position of each part constituting thebicycle is specified as a bounding box in the overall image of thebicycle and the class specifying the part is labeled. With the trainedfirst neural network, when the bicycle image is input to the firstneural network, the first neural network may extract feature values ofthe input image through a convolution operation, specify the positionsof the parts of the bicycle included in the input image based on theextracted feature values, and output specification information mapped tothe specified parts.

For example, the first neural network may specify and classifyindividual parts (for example, frame, crank, front derailleur, rearderailleur, wheel, lever, or the like) from the entire image of thebicycle, and may group a set (for example, frameset, drivetrain,wheelset, component) of individual parts which are operated inconjunction with each other. The neural network module 122 may storeinformation on parts included in the input bicycle image in the form ofa set based on the classified individual parts and sets of individualparts. For example, the neural network module 122 may group and specifythe information of the parts included in the input bicycle images in theform of a set (fork, frame) of elements corresponding to the frameset, aset (front derailleur, rear derailleur, brake, lever, crank, cassette,chain) of elements corresponding to the group set, a set (tire, hub) ofelements corresponding to the wheelset, and a set (stem, seat-post,handlebar, saddle) of elements corresponding to the component.

The neural network module 122 may search the specification informationDB 112 for specification information corresponding to each specifiedpart, and map and store the specification information corresponding toeach specified part.

FIG. 3 is an exemplary diagram illustrating an image which is a resultof detecting an abnormal area of a part according to one exemplaryembodiment and is used for training a second neural network fordetermining a type of the abnormal area.

Referring to FIG. 3 , the image used for the training of the secondneural network may include an image directly labeled for training, or animage in which the abnormal area is automatically specified according tothe embodiment (for example, steps S1010 to S1040 in FIG. 4 ) of thepresent disclosure and the type of the abnormal area is labeled. Theclass for the type of the abnormal area may include classes for pores,multi-pores, paint repair, resin excess, delamination, fiber damage,fiber mismatch, fiber repair, heat damage, foreign matter, joint damage,wrinkling, and disengagement.

Referring to FIG. 3 , the neural network module 122 may train the secondneural network by using a predetermined image recognition algorithm withthe training images in each of which the abnormal area is specified asthe bounding box and the class for the type of abnormal area is labeled.With the trained second neural network, when the image in which theabnormal area is specified is input to the second neural network, thesecond neural network may extract feature values of the input imagethrough a convolution operation, and specify the position and type ofthe abnormal area included in the input image based on the extractedfeature values.

The image module 123 may generate the gray scale image of thephotographed bicycle part based on the information on the transmissionamount obtained from the non-destructive imaging device.

The control module 124 may analyze the generated gray scale image todetermine whether the abnormal area is included in the photographedpart, and may specify the type of the determined abnormal area. Thecontrol module 124 may determine a repair method based on the type ofthe abnormal area or calculate the price of the corresponding part.

Operations performed by the inspection module 121, the neural networkmodule 122, the image module 123, and the control module 124 describedabove may be understood as operations performed by the processor 120.

The input/output interface 130 may include a hardware interface or asoftware interface that allows a manager who controls thenon-destructive inspection apparatus 100 to input specific informationor outputs specific information to the manager.

The communication interface 140 enables the non-destructive inspectionapparatus 100 to transmit and receive information to and from anexternal device (for example, non-destructive imaging device) through acommunication network. To this end, the communication interface 140 mayinclude a wireless communication module or a wired communication module.

The non-destructive inspection apparatus 100 may be implemented asvarious types of apparatuses capable of performing an operation throughthe processor 120 and transmitting/receiving information through anetwork. For example, the non-destructive inspection apparatus 100 mayinclude a portable communication device, a smart phone, a computerdevice, a portable multimedia device, a notebook computer, a tablet PC,or the like. Hereinafter, an embodiment of an operation performed by thenon-destructive inspection apparatus 100 through the above-describedconfiguration will be described along with FIGS. 4 to 11 .

FIG. 4 is a flowchart of an operation in which the non-destructiveinspection apparatus 100 performs a non-destructive inspection methodaccording to one embodiment.

Referring to FIG. 4 , the inspection module 121 may control thenon-destructive imaging device to emit X-rays to the part of thebicycle, and obtain information on the amount of X-rays transmittedthrough the part of the bicycle (step S1010).

The inspection module 121 performs an X-ray control for each part of thebicycle so that the control module 124 or the neural network module 122detects the abnormal portion with high accuracy and generatesinformation that is easy to determine the abnormal area. This isbecause, as illustrated in FIGS. 5A to 5C, information on the abnormalarea may be included or lost depending on how the properties of theX-rays are controlled.

FIGS. 5A to 5C are exemplary diagrams of gray scale images respectivelygenerated from information obtained by changing the properties of theX-rays for the same object.

Referring to FIGS. 5A to 5C, when the transmission amount of X-rays istoo low, an image such as FIG. 5A is generated, and when thetransmission amount of the X-rays is too high, an image such FIG. 5C isgenerated. Accordingly, it is important to obtain an image such as FIG.5B by appropriately controlling the properties of the X-rays dependingon each part.

The inspection module 121 may adjust the properties of the X-rays foreach part based on the characteristics of the corresponding part that isa target to be photographed. For example, when the inspection module 121photographs the parts of the bicycle formed of carbon, the inspectionmodule 121 may control the non-destructive imaging device to emit theX-rays so that a voltage in a range from 60 kV to 70 kV, a current in arange from 11.0 mA to 12.0 mA, and a focal point (FOC) in a range from0.4 mm to 1.0 mm are set during the radiation of the X-rays.

Before the inspection module 121 performs the X-ray photographingoperation, the neural network module 122 may photograph the entire imageof the bicycle that is a non-destructive inspection target and input thephotographed image to the first neural network model trained with theimage data set for respective parts of the bicycle to which pieces ofthe specification information for the respective parts of the bicycleare mapped. Then, the neural network module 122 may specify thepositions and types of the parts of the bicycle to be inspected. Theinspection module 121 may search the specified specification informationfor each part in the specification information DB 112. The specificationinformation for each part may include information on the voltage,current, and focal length of X-rays that are preset to performnon-destructive inspection on each part. The inspection module 121 maycontrol properties of X-rays based on the information stored in thespecification information by adjusting a X-ray emission position to aposition of a specified part, and thus, obtain the information of thetransmission amount suitable for analysis for each part.

Next, the image module 123 may generate the gray scale image based onthe acquired information of the transmission amount (step S1020).

The image module 123 determines which range of the acquired informationon the transmission amount is converted into the gray scale to allow thecontrol module 124 or the neural network module 122 to detect theabnormal portion with high accuracy, and creates an image that is easyto determine the abnormal area. This is because the information on theabnormal area may be included or lost depending on which range of theacquired information on the transmission amount is converted into thegray scale.

FIG. 6 is an exemplary diagram of the gray scale images in which thepart of the bicycle is converted into the gray scale by changing anintensity of the transmission amount of X-rays.

Referring to FIG. 6 , the image module 123 may rescale an intensity ofthe transmission amount of the X-rays into an arbitrary unit intensity(a.u. intensity) between a minimum value of 0 and a maximum value of3500, and select, depending on the part of the bicycle, one template ofa first template, a second template, a third template, a fourthtemplate, and a fifth template. When the first template is selected,only portions of the part having rescaled intensities of thetransmission amount of the X-ray ranging from 2216 to 2500 are convertedinto the gray scale. When the second template is selected, only portionsof the part having rescaled intensities of the transmission amount ofthe X-ray ranging from 2108 to 3294 are converted into the gray scale.When the third template is selected, only portions of the part havingrescaled intensities of the transmission amount of the X-ray rangingfrom 1893 to 2878 are converted into the gray scale. When the fourthtemplate is selected, only portions of the part having resealedintensities of the transmission amount of the X-ray ranging from 1257 to2878 are converted into the gray scale. When the fifth template isselected, only portions of the part having resealed intensities of thetransmission amount of the X-ray ranging from 31 to 2410 are convertedinto the gray scale.

For example, since a dark gray scale image with high contrast isgenerated by the conversion using the first template, the first templatemay be stored in the specification information such that the firsttemplate is used for inspection of a part of which a cross section or aside surface such as a thickness or a shape of an outline is needed tobe checked.

Further, since a dark gray scale image with little contrast is generatedby the conversion using the second template, the second template may bestored in the specification information such that the second template isused for inspection of a part of which the state of the surface isneeded to be carefully checked.

Further, since a gray scale image having the contrast and brightness ina balanced way is generated by the conversion using the third template,the third template may be stored in the specification information suchthat the third template is used for inspection of a part of which allelements of all surfaces and lines are needed to be inspected.

Further, since a bright gray scale image with a relatively largecontrast is generated by the conversion using the fourth template, thefourth template may be stored in the specification information such thatthe fourth template is used for inspection of a part having a relativelylarge thickness or low X-ray transmission due to an overlappingstructure.

Further, since a bright gray scale image with little contrast isgenerated by the conversion using the fifth template, the fifth templatemay be stored in the specification information such that the fifthtemplate is used for inspection of a part manufactured by mixing metalelements and three or more overlapped thick parts.

Next, the control module 124 may measure an amount of change in the grayvalue from one end to the other end of the part of the bicyclerepresented in the gray scale image along the extending direction of thepart (step S1030), and detect an area where the amount of change in thegray value is equal to or greater than a threshold as the abnormal area(step S1040).

FIG. 7 is an exemplary diagram for explaining an operation of measuringthe amount of change in the gray value according to one embodiment whilecontrolling a cutting line located at one end ‘a’ of the part of thebicycle to move to the other end ‘b’ of the part of the bicycle alongthe extending direction of the part.

Referring to FIG. 7 , the control module 124 may measure a value (grayvalue) of the gray scale along a cutting line orthogonal to the outersurface of the part recognized in the gray scale image.

FIGS. 8A to 8C are exemplary diagrams for explaining an operation ofdetecting the part in a normal state according to one embodiment. Forthe sake of convenience of understanding, FIGS. 8A to 8C show examplesof measuring the values of the gray scale measured at each of thecutting lines of three portions a, b, and c spaced apart from eachother.

Referring to each of FIGS. 8A to 8C, the control module 124 may measurethe values of the gray scale along the cutting line orthogonal to theouter surface of the part recognized in the gray scale image. Referringto the graphs for the measurements at the three portions a, b, and c ofFIGS. 8A to 8C, the value of the gray scale measured at the cutting lineof each portion is not abruptly changed but is substantially maintainedconstant.

FIGS. 9A to 9C are exemplary diagrams for explaining an operation ofmeasuring the part including the abnormal area according to oneembodiment. For the sake of convenience of understanding, FIGS. 9A to 9Cshow examples of measuring the values of the gray scale measured at eachof the cutting lines of the three portions a, b, and c spaced apart fromeach other.

Referring to FIGS. 9A to 9C, the control module 124 may measure thevalues of the gray scale along the cutting line orthogonal to the outersurface of the part recognized in the gray scale image. Referring to thegraphs for the measurements at the three portions a, b, and c of FIGS.9A to 9C, compared to the shape of the graph for the cutting line of theportion a or c, it was found that the value of the gray scale isabruptly changed in one section of the graph for the cutting line of theportion b.

In one embodiment of the present invention, paying attention to the factthat one or more sections in which the value of the gray scale isabruptly changed are present in the abnormal area of the gray scaleimage photographed by the X-rays having suitable properties, theabnormal area may be detected based on the continuously measured amountof change in the gray value as illustrated in FIG. 10 .

FIG. 10 is an exemplary diagram of a three-dimensional graph informationgenerated based on the amount of change in the gray value measuredaccording to one embodiment.

Referring to FIG. 10 , the control module 124 may generate athree-dimensional graph information in which the amount of change in thegray value is measured with a Z-axis representing a length of thecutting line cutting the part at the one end of the part, an x-axisrepresenting a movement distance of the cutting line from the one end tothe other end along the extending direction of the part, and a y-axisrepresenting a value of the gray scale measured while the cutting linemoves on the x-axis. When there is at least one area where the amount ofchange in the y-axis within a predetermined range of the x-axis amongthe three-dimensional graph information is equal to or greater than apreset threshold, the control module 124 may detect, as the abnormalarea, a portion corresponding to an x-axis length at which the amount ofchange in the y-axis that is equal to or greater than the presetthreshold is generated. Moreover, the control module 124 may detect, asthe abnormal area, a three-dimensional area including the x-axis lengthand a z-axis length at which the amount of change in the y-axis equal toor greater than the preset threshold is generated.

FIG. 11 is an exemplary diagram for explaining an operation of measuringthe amount of change in the gray value while controlling a point locatedat one end ‘a’ of the part of the bicycle to move to the other end ‘b’of the part of the bicycle along the extending direction of the part,according to one embodiment.

Referring to FIG. 11 , the control module 124 may measure the values ofthe gray scale along a direction parallel to the outer surface of thepart recognized in the gray scale image. The control module 124 maygenerate two-dimensional graph information in which the amount of changein the gray value is measured with an x-axis representing a movementdistance of one point from the one end to the other end along theextending direction of the part and a y-axis representing a value of thegray scale measured when the one point is controlled to move on thex-axis. When there is at least one point at which the amount of changein the y-axis within a predetermined range of the x-axis among thetwo-dimensional graph information is equal to or greater than a presetthreshold, the control module 124 may detect, as the abnormal area, aportion corresponding to an x-axis length at which the amount of changein the y-axis that is equal to or greater than the preset threshold isgenerated.

Next, the control module 124 may determine the type of the abnormal areabased on an image of the specified abnormal area (step S1050).

For example, the control module 124 may determine the class of theabnormal area by inputting the image of the detected abnormal area tothe second neural network model that is trained with the abnormal imagedata set for each part of the bicycle. The control module 124 maycalculate the price by reflecting depreciation information obtainedbased on the class of the abnormal area to the specification informationof the part including the abnormal area. For example, by reflectingdepreciation information (for example, a price drop of 10% when theclass of the detected abnormal area is the pore, or a price drop of 15%when the class of the detected abnormal area is the joint) to anoriginal price derived based on the specification information of thespecified part, it is possible to recalculate the price according to theexistence of the abnormal area. The control module 124 may search thespecification information DB 112 to find a repair method according tothe type of the abnormal area of the corresponding part, and output therepair method or the repair price of the abnormal area of thecorresponding part.

According to the above-described embodiments, it is possible to simplydetect an abnormal portion of the part without disassembling thebicycle, and it is possible to determine the type of the abnormalportion. Accordingly, it is possible to improve the inspection accuracywhile effectively shortening the inspection process during manufacturingof the bicycle. In addition, by allowing a certified institution to usethe technique of the present disclosure, it becomes possible to certifythe quality of bicycles in the second-hand market, thereby establishinga healthy trading culture and ensuring the safety of bicycle users.

Various embodiments of the present disclosure and terms used therein arenot intended to limit the technical characteristics disclosed in thepresent disclosure to the specific embodiments, which should beunderstood to include various modifications, equivalents, or substitutesof the corresponding embodiment. Regarding the description of thedrawings, like reference numerals may be given to substantially likeparts. A singular expression of a noun corresponding to an item mayinclude one or more items unless relevant context explicitly dictatesotherwise.

In the present disclosure, each of the phrases such as “A or B,” “atleast one of A and B,” “at least one of A or B,” “A, B, or C,” “at leastone of A, B, and C,” and “at least one of A, B, or C” may include allpossible combinations of the items listed together in the correspondingphrase. Terms such as “first” or “second” may be used simply todistinguish one constituting element from the other constituting elementand do not limit the corresponding constituting elements in favor ofanother aspect (for example, importance or order). When a particular(for example, first) constituting element is said to be “coupled” or“connected” to another (for example, second) constituting element withor without the term “functionally” or “communicatively,” it means thatthe particular constituting element may be linked to anotherconstituting element directly (for example, in a wired manner),wirelessly, or through a third constituting element.

The term “module” used in the present disclosure may include a unitimplemented using hardware, software, or firmware. For example, the term“module” may be used interchangeably with a term such as logic, logicblock, component, or circuit. A module may be an integral component or aminimum unit of a component or a part thereof that performs one or morefunctions. For example, according to one embodiment, a module may beimplemented in the form of application-specific integrated circuit(ASIC).

Various embodiments of the present disclosure may be implemented assoftware (for example, a program) including one or more instructionsstored in a storage medium (for example, memory) readable by a device(for example, an electronic device). The storage medium may include arandom access memory (RAM), a memory buffer, a hard drive, a database,an erasable programmable read-only memory (EPROM) an electricallyerasable read-only memory (EEPROM), a read-only memory (ROM) and/or thelike.

Further, the processor according to the embodiments of the presentdisclosure may call at least one instruction among one or more storedinstructions from the storage medium and execute the instruction called.This operation enables a device to perform at least one functionaccording to at least one instruction called. The one or moreinstructions may include code generated by a compiler or code executableby an interpreter. The processor may be a general-purpose processor, afield programmable gate array (FPGA), the ASIC, a digital signalprocessor (DSP) and/or the like.

A machine-readable storage medium may be provided in the form of anon-transitory storage medium. Here, the term “non-transitory” onlyindicates that the storage medium is a tangible device but does notinclude a signal (for example, electromagnetic waves). The term“non-transitory”, therefore, does not distinguish a case in which datais stored in a storage medium semi-permanently from a case in which datais stored temporarily.

Methods according to various embodiments of the present disclosure maybe provided by being included in a computer program product. Thecomputer program product may be traded between sellers and buyers as acommodity. The computer program product may be distributed in the formof a machine-readable storage medium (for example, a CD-ROM) or directlyonline (for example, download or upload) through an application store(for example, Play Store) or between two user devices (for example,smartphones). In the case of online distribution, at least part of thecomputer program product may be at least stored temporarily or generatedtemporarily in a server of the manufacturer, a server of the applicationstore, or a machine-readable storage medium such as a memory of a relayserver.

According to various embodiments, each of the constituting elements (forexample, a module or a program) may be composed of single or multipleentities. According to various embodiments, one or more constitutingelements or operations may be omitted from among the correspondingconstituting elements described above, or one or more other constitutingelements or operations may be added. Alternatively or additionally, aplurality of constituting elements (for example, a module or a program)may be integrated into a single constituting element. In this case, theintegrated constituting elements may perform one or more functions ofeach of the plurality of constituting elements in the same manner or ina similar manner as performed by the corresponding constituting elementof the plurality of constituting elements before the integration.According to various embodiments, the operations executed by a module, aprogram, or another constituting element may be performed in asequential, parallel, repetitive, or heuristic manner; or one or moreoperations may be performed in a different order or omitted, or one ormore different operations may be added to the operations.

What is claimed is:
 1. A non-destructive inspection apparatus,comprising: at least one memory configured to store commands forperforming predetermined operations; and at least one processoroperatively coupled to the at least one memory and configured to executethe commands, wherein the at least one processor is configured to:obtain information on a transmission amount of an X-ray by emitting theX-ray to a target product for non-destructive inspection, generate agray scale image based on the information on the transmission amount,measure a gray scale from a first end to a second end of the targetproduct represented in the gray scale image along an extending directionof the target product, and detect an abnormal area based on an amount ofchange in the gray scale, wherein, in obtaining the information on thetransmission amount, the at least one processor is further configuredto: specify a part of the target product by inputting an entire image ofthe target product to a first neural network model that is trained withan image data set for each part of the target product to whichspecification information for a corresponding part of the target productis mapped; and obtain information on the transmission amount of theX-ray for the specified part by adjusting an X-ray emission position toa position of the specified part.
 2. The non-destructive inspectionapparatus of claim 1, wherein the X-ray is emitted with a voltage in arange from 60 kV to 70 kV, a current in a range from 11.0 mA to 12.0 mA,and a focal point (FOC) in a range from 0.4 mm to 1.0 mm so as to obtainthe gray scale of the target product formed of carbon.
 3. Thenon-destructive inspection apparatus of claim 1, wherein, in generatingthe gray scale image, the at least one processor is further configuredto rescale an intensity of the transmission amount of the X-ray into anarbitrary unit intensity (a.u. intensity) between a minimum value of 0and a maximum value of 3500, and select one template of a firsttemplate, a second template, a third template, a fourth template, and afifth template depending on a type of the target product, wherein whenthe first template is selected, only portions of the target producthaving rescaled intensities of the transmission amount of the X-rayranging from 2216 to 2500 are converted into the gray scale; when thesecond template is selected, only portions of the target product havingrescaled intensities of the transmission amount of the X-ray rangingfrom 2108 to 3294 are converted into the gray scale; when the thirdtemplate is selected, only portions of the target product havingrescaled intensities of the transmission amount of the X-ray rangingfrom 1893 to 2878 are converted into the gray scale; when the fourthtemplate is selected, only portions of the target product havingrescaled intensities of the transmission amount of the X-ray rangingfrom 1257 to 2878 are converted into the gray scale; and when the fifthtemplate is selected, only portions of the target product havingresealed intensities of the transmission amount of the X-ray rangingfrom 31 to 2410 are converted into the gray scale.
 4. Thenon-destructive inspection apparatus of claim 1, wherein, in measuringthe gray scale, the at least one processor is further configured togenerate two-dimensional graph information in which the amount of changein the gray scale is measured with an first axis representing a movementdistance of one point from the first end to the second end along theextending direction of the target product and a second axis representinga value of the gray scale measured when the one point is controlled tomove on the first axis.
 5. The non-destructive inspection apparatus ofclaim 4, wherein, in detecting the abnormal area, when the amount ofchange in the gray scale in the second axis within a predetermined rangeof the first axis is equal to or greater than a preset threshold, the atleast one processor is further configured to detect, as the abnormalarea, a portion corresponding to an first axis length at which theamount of change in the gray scale in the second axis that is equal toor greater than the preset threshold is generated.
 6. Thenon-destructive inspection apparatus of claim 1, wherein, in measuringthe gray scale, the at least one processor is further configured togenerate a three-dimensional graph information in which the amount ofchange in the gray scale is measured with a third axis representing alength of a cutting line cutting the target product at the first end, anfirst axis representing a movement distance of the cutting line from thefirst end to the second end along the extending direction of the targetproduct, and a second axis representing a value of the gray scalemeasured while the cutting line is controlled to move on the first axis.7. The non-destructive inspection apparatus of claim 6, wherein, indetecting the abnormal area, when the amount of change in the gray scalein the second axis within a predetermined range of the first axis isequal to or greater than a preset threshold, the at least one processoris further configured to detect, as the abnormal area, a portioncorresponding to an first axis length at which the amount of change inthe gray scale in the second axis that is equal to or greater than thepreset threshold is generated.
 8. The non-destructive inspectionapparatus of claim 7, wherein, in detecting the abnormal area, the atleast one processor is further configured to detect, as the abnormalarea, a three-dimensional area including the first axis length and athird axis length at which the amount of change in the gray scale in thesecond axis is that is equal to or greater than the preset threshold isgenerated.
 9. The non-destructive inspection apparatus of claim 1,wherein the specification information includes information specifying atype of the part and information on a voltage, a current, a focal lengthof the X-ray which are preset to perform non-destructive inspection onthe part.
 10. The non-destructive inspection apparatus of claim 1,wherein the first neural network model is trained based on an imagerecognition algorithm, and the image data set for each part of thetarget product includes a data set in which a frame, a wheel, and adrivetrain of the target product are labeled in portions of an image.11. A non-destructive inspection apparatus, comprising: at least onememory configured to store commands for performing predeterminedoperations; and at least one processor operatively coupled to the atleast one memory and configured to execute the commands, wherein the atleast one processor is configured to: obtain information on atransmission amount of an X-ray by emitting the X-ray to a targetproduct for non-destructive inspection, generate a gray scale imagebased on the information on the transmission amount, measure a grayscale from a first end to a second end of the target product representedin the gray scale image along an extending direction of the targetproduct, and detect an abnormal area based on an amount of change in thegray scale, wherein the at least one processor is further configured todetermine a type of the abnormal area based on an image of the abnormalarea after performing the detecting of the abnormal area, wherein, indetermining the type of the abnormal area, the at least one processor isfurther configured to determine a class of the abnormal area byinputting the image of the abnormal area to a second neural networkmodel that is trained with an abnormal image data set for each part ofthe target product.
 12. The non-destructive inspection apparatus ofclaim 11, wherein, in determining the type of the abnormal area, the atleast one processor is further configured to: calculate a price of thetarget product by reflecting depreciation information obtained based onthe class of the abnormal area to specification information of a partincluding the abnormal area, and wherein the specification informationincludes information specifying the price of the part.
 13. Thenon-destructive inspection apparatus of claim 12, wherein the secondneural network model is trained based on an image recognition algorithm,and the abnormal image data set includes a data set in which breakage,repair, reinforcement, joint, and pores of the target product arelabeled in portions of the image.
 14. A non-destructive inspectionmethod performed by a non-destructive inspection apparatus, thenon-destructive inspection method comprising: acquiring information on atransmission amount of an X-ray by emitting the X-ray to a targetproduct for non-destructive inspection; generating a gray scale imagebased on the information on the transmission amount; measuring a grayscale from a first end to a second end of the target product representedin the gray scale image along an extending direction of the targetproduct; and detecting an abnormal area based on an amount of change inthe gray scale, wherein the acquiring the information on thetransmission amount includes: specifying a part of the target product byinputting an entire image of the target product to a first neuralnetwork model that is trained with an image data set for each part ofthe target product to which specification information for acorresponding part of the target product is mapped, and obtaininginformation on the transmission amount of the X-ray for the specifiedpart by adjusting an X-ray emission position to a position of thespecified part.